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CRII: CHS: RUI: Computational models of humans for studying and improving Human-AI interaction

CRII：CHS：RUI：用于研究和改善人机交互的人类计算模型

基本信息

批准号：
2218226
负责人：
Christopher Dancy
金额：
$ 17.41万
依托单位：
Pennsylvania State Univ University Park
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-01-15 至 2023-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2218226&HistoricalAwards=false
关键词：
CRII CHS RUI Computational models

项目摘要

Understanding the interactions between humans and systems utilizing artificial intelligence (AI) requires an understanding of how human physiological changes impact memory and other mental processes. To realize the beneficial societal outcomes on, for example, interactions between humans and intelligent robots, it is important to develop simulations to test a variety of situations where memory under arousal states will function. In this project, the investigator articulates a research plan to use a simulation of the human mind and body to understand effects of physiological arousal on human memory and cognition and the consequences for human interaction with AI agents. Human-subject studies will be used to introduce stimuli to induce human arousal by using selected stressors and collect physiological and behavioral data during tasks that require cooperation between humans and AI agents. Societal benefits include an architecture to simulate a variety of human-AI interactions under various levels of arousal and stress. This architecture can be used to explore new ways to co-team humans with AI agents and set expectations for positive and/or negative behaviors that might occur in such collaborations. Undergraduate students at Bucknell University will be heavily involved as research assistants on this project. The investigator plans to develop simulations and elicit arousal states in human operators performing collaborative tasks with agents enabled by artificial intelligence (AI) to understand human-AI interaction. The goal is to better understand, through simulation, how algorithms for intelligent agents can be advanced and expanded to respond to human variations in behavior and memory processes under different levels of arousal, including levels that mimic stress. The objectives include developing simulations to examine contexts and tasks; understanding how environmental stimuli affect interactions between humans and intelligent agents; and determining how to advance algorithms to optimize human-AI cooperation and avoid maladaptive interactions. The investigator has already extended Adaptive Control of Thought-Rational (ACT-R) architecture to account for physiological influences on declarative and procedural memory. Physio-cognitive agents will be developed based on Bayesian and reinforcement learning to acquire knowledge of their environment. Human-AI task simulations will be implemented in a virtual environment to understand how arousal mediates intelligent behavior and how interaction with external environments that include AI agents may change performance, including through maladaptive behavior. The project seeks to discover computationally-enabled processes and contexts to amplify human capabilities. The resulting revised models and open-source code will be made available to the public for further explorations in human-AI interaction. The university is an undergraduate institution allowing significant participation of undergraduates in research.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

理解人类与利用人工智能（AI）的系统之间的相互作用需要理解人类生理变化如何影响记忆和其他心理过程。为了实现有益的社会结果，例如，人类与智能机器人之间的互动，开发模拟来测试唤醒状态下记忆功能的各种情况是很重要的。在这个项目中，研究者阐明了一项研究计划，利用人类身心的模拟来了解生理唤醒对人类记忆和认知的影响，以及人类与人工智能代理互动的后果。人类受试者研究将用于引入刺激，通过使用选定的压力源来诱导人类觉醒，并在需要人类和人工智能代理之间合作的任务中收集生理和行为数据。社会效益包括一个架构，可以模拟在不同程度的兴奋和压力下的各种人类与人工智能的互动。这种架构可以用来探索人类与人工智能代理合作的新方法，并为这种合作中可能出现的积极和/或消极行为设定预期。巴克内尔大学的本科生将作为研究助理大量参与该项目。研究人员计划开发模拟并引发人类操作员与人工智能（AI）启用的代理执行协作任务时的唤醒状态，以了解人类与AI的交互。目标是通过模拟，更好地理解智能代理的算法如何被改进和扩展，以应对人类在不同唤醒水平（包括模拟压力水平）下的行为和记忆过程的变化。目标包括开发模拟来检查上下文和任务；了解环境刺激如何影响人类与智能体之间的互动；并决定如何改进算法以优化人类与人工智能的合作，避免不适应的互动。研究者已经扩展了思维-理性的适应性控制（ACT-R）架构，以解释对陈述性和程序性记忆的生理影响。物理认知代理将基于贝叶斯和强化学习来获得环境知识。人类-人工智能任务模拟将在虚拟环境中实现，以了解唤醒如何调节智能行为，以及与包括人工智能代理在内的外部环境的交互如何改变性能，包括通过适应不良行为。该项目旨在发现计算支持的过程和背景，以增强人类的能力。由此产生的修订模型和开源代码将向公众开放，以进一步探索人类与人工智能的互动。这所大学是一所本科院校，允许本科生大量参与研究。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。